The goal of oolong [1] is to generate and adminstrate validation tests easily for typical automated content analysis tools such as topic models and dictionary-based tools.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("chainsawriot/oolong")newsgroup_stm is an example topic model trained with the data newsgroup5 using the stm package. Currently, this package supports structural topic models / correlated topic models from stm and Wrap LDA models from text2vec.
library(oolong)
library(stm)
#> stm v1.3.5 successfully loaded. See ?stm for help.
#> Papers, resources, and other materials at structuraltopicmodel.com
newsgroup_stm
#> A topic model with 10 topics, 4182 documents and a 8920 word dictionary.To create an oolong test, use the function create_oolong_test.
oolong_test <- create_oolong(newsgroup_stm)
oolong_test
#> An oolong test object with k = 10, 0 coded. (0% accuracy)
#> Use the method $do_word_intrusion_test() to start coding.As instructed, use the method $do_word_intrusion_test() to start coding. If you are running this in RStudio, you should see a test screen similar to this:
After the coding, you can look at the accuracy rate by printing the oolong test.
oolong_test[1] /ˈuːlʊŋ/ 烏龍, literally means "Dark Dragon", is a semi-oxidized tea from Asia. It is very popular in Taiwan, Japan and Hong Kong